Hanaa E. Sayed

A hybrid statistical genetic-based demand forecasting expert system

Demand forecasting is considered a key factor for balancing risk of over-stocking and out-of-stock. It is the main input to supply chain processes affecting their performance. Even with much effort and funds spent to improve supply chain processes, they still lack reliability and efficiency if the demand forecast accuracy is poor. This paper presents a proposal of an integrated model of statistical methods and improved genetic algorithm to generate better demand forecast accuracy. An improved genetic algorithm is used to choose the best weights among the statistical methods and to optimize the forecasted activities combinations that maximize profit. A case study is presented using different product types. And, a comparison is conducted between results obtained from the proposed model and from traditional statistical methods, which demonstrates improved forecast accuracy using the proposed model for all time series types.

Design of fault simulator

Fault simulator is proposed to understand and evaluate all possible fault propagation scenarios, which is an essential part of safety design and operation design and support of chemical/production processes. Process models are constructed and integrated with fault models, which are formulated in qualitative manner using fault semantic networks (FSN). Trend analysis techniques are used to map real time and simulation quantitative data into qualitative fault models for better decision support and tuning of FSN. The design of the proposed fault simulator is described and applied on experimental plant (G-Plant) to diagnose several fault scenarios. The proposed fault simulator will enable industrial plants to specify and validate safety requirements as part of safety system design as well as to support recovery and shutdown operation and disaster management.

Trend analysis using real time fault simulation for improved fault diagnosis

Early fault detection is critical for safe and optimum plant operation and maintenance in any chemical plant. Quick corrective action can help in minimizing quality and productivity offsets and can assist in averting hazardous consequences in abnormal situations. In this paper, fault diagnosis based on trends analysis is considered where integrated equipment behaviors and operation trajectory are analyzed using a trend-matching approach. A qualitative representation of these trends using IF-THEN rules based on neuro-fuzzy approach is used to find root causes and possible and consequences for any detected abnormal situation. Experimental plant is constructed to provide real time fault simulation data for fault detection method verification.

Analytical process and system design of integrated fault diagnostic system

There is an increasing interest to find an effective mechanism for fault diagnosis, which is essential for safe plant operation and optimised maintenance. This paper presents an integrated framework for qualitative and quantitative fault diagnosis mechanism where qualitative fault models are developed for the underlying plant process and equipment and linked with quantitative methods on the basis of trend analysis. The proposed fault diagnosis process is described using detailed activity models. The proposed integrated fault diagnosis framework is illustrated using a case study experimental plant G-Plant, which showed improved fault diagnosis capabilities in terms of root cause and consequence analysis.

Computer-Aided Modeling & Simulation Environment for Green Energy Production Chain Planning

There are major challenges for current energy production where cost and environmental impacts are forcing energy producers to find alternative solutions. This research paper presents intelligent algorithm and modeling and simulation methodology to evaluate new energy production scenarios. Genetic algorithm method is used to select the most optimum solution in terms of cost and environmental impacts based on proposed energy. On the basis of POOM process object-oriented modeling methodology, scenario generation algorithm is proposed where geographical information is managed within GIS or geographical information system. Case study is used to present the proposed energy planning approach to meet new energy requirements.

Design of Demand Forecasting Expert System for Dynamic Supply Chains

When distributors and wholesalers seek help with issues relating to inventory management, they are usually concerned about an increasing level of out-of-stocks or over stocking. Out of stocks are leading to sales loss and customer service complaints. Over-stocks are resulting in slow inventory turnover and a buildup of dead inventory. In fact, out-of-stocks and overstocks are actually a flip side of the same inventory management coin. Any effective initiative to resolve these issues must address core structural causes of these inventory management problems. Superior inventory management begins with timely, accurate, detailed demand forecasts. Over last decade demand forecasting has played a prominent role in the corporations worldwide. Corporate executives have spent millions of dollars and invested thousands of man-hours trying to improve methods used & complicate it more. In each case little attention was paid to the integration between drivers, inputs and demand forecast (Harrison & Qizhong, 1993). In the face of all these advancements in hardware and software forecast error still remain high. The inaccuracy in the forecast is due to previous researchers focused on statistical methods and their improvements only. There was no effort on the modeling of the problem and how to build an expert system to interact properly with the dynamic changes of the supply chain (Ajoy & Dobrivoje, 2005). The forecasting model is not treated as enterprise system has its specifications and constraints which are modeled and simulated. In this research we propose a design of expert demand forecast system which is designed after deep understanding of demand cycle within dynamic supply chain and interaction between different parameters within the supply chain. It is utilizing Bayesian vector auto regression, restricted vector auto regression, and kernel fisher discriminant analysis (Scholkopf & Smola, 1998), (Scholkopf et al., 1999) with improved genetic algorithm to filter, analyze inputs and factors affecting demand along with demand history and then generate baseline and operational forecasts. This model proposes new mathematical and expert modeling methodology to generate forecasts. We used a practical case study from international FMCG (Fast Moving Consumer Goods) industry using over 1000 product types and results show that a significant forecast accuracy and other supply chain key performance indicators improvements over one year months rolling. The proposed model is composed of the integration between statistical and intelligent methods with expert input to generate more accurate demand forecasts. The inputs to the Source: Expert Systems, Book edited by: Petrică Vizureanu, ISBN 978-953-307-032-2, pp. 238, January 2010, INTECH, Croatia, downloaded from SCIYO.COM

Neurofuzzy-based learning algorithm for fault detection & simulation

Early fault detection is critical for safe and optimum plant operation and maintenance in any chemical plant. Quick corrective action can help in minimizing quality and productivity offsets and can assist in averting hazardous consequences in abnormal situations. In this paper, fault diagnosis based on trends analysis is considered where integrated equipment behaviors and operation trajectory are analyzed using a trend-matching approach. A qualitative representation of these trends using IF-THEN rules based on neuro-fuzzy approach is used to find root causes and possible and consequences for any detected abnormal situation. Experimental plant is constructed to provide real time fault simulation data for fault detection method verification.

Agent Model for Human Expert Trend Analysis Technique for Real Time Fault Simulation in Integrated Fault Diagnostic System

Early fault detection is critical for safe and optimum plant operation and maintenance in any chemical plant. Quick corrective action can help in minimizing quality and productivity offsets and can assist in averting hazardous consequences in abnormal situations. In this paper, fault diagnosis based on trends analysis is considered where integrated equipment behaviors and operation trajectory are analyzed using a trend-matching approach. A qualitative representation of these trends using IF- THEN rules based on neuro-fuzzy approach is used to find root causes and possible and consequences for any detected abnormal situation. Experimental plant is constructed to provide real time fault simulation data for fault detection method verification.